CryptoPulse: Short-Term Cryptocurrency Forecasting with Dual-Prediction and Cross-Correlated Market Indicators

TL;DR

CryptoPulse introduces a dual-prediction model that integrates macroeconomic factors, technical indicators, and market sentiment to improve short-term cryptocurrency price forecasts, outperforming existing methods.

Contribution

The paper presents a novel dual prediction framework with a refinement mechanism that incorporates diverse market indicators for enhanced accuracy.

Findings

Achieves state-of-the-art forecasting accuracy

Outperforms ten comparison methods

Effectively integrates macro, technical, and sentiment data

Abstract

Cryptocurrencies fluctuate in markets with high price volatility, posing significant challenges for investors. To aid in informed decision-making, systems predicting cryptocurrency market movements have been developed, typically focusing on historical patterns. However, these methods often overlook three critical factors influencing market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations affecting collaborative investor behaviors; 2) overall market sentiment, heavily influenced by news impacting investor strategies; and 3) technical indicators, offering insights into overbought or oversold conditions, momentum, and market trends, which are crucial for short-term price movements. This paper proposes a dual prediction mechanism that forecasts the next day's closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Additionally, a novel refinement mechanism enhances predictions through market sentiment-based rescaling and fusion. Experiments demonstrate that the proposed model achieves state-of-the-art performance, consistently outperforming ten comparison methods.